Erratum to: Peritumoral apparent diffusion coefficients for prediction of lymphovascular invasion in clinically node-negative invasive breast cancer

Mori, Nobuhiro; Mugikura, Shunji; Takasawa, Chiaki; Miyashita, Minoru; Shimauchi, Akiko; Ota, Hideki; Ishida, Toru; Kasajima, Atsuko; Takase, Kei; Kodama, Tetsuya; Takahashi, Satoshi

doi:10.1007/s00330-015-3888-8

Cited by 2 publications

(2 citation statements)

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“…Many studies have revealed that LVI is an independent risk factor for survival in patients with ESCC (44,48). Preoperative prediction of LVI status is necessary for patients to implement an aggressive treatment plan (49). Patients with suspected tumor microvascular invasion require more advanced treatment, such as more extensive surgery or preoperative adjuvant therapy (50).…”

Section: Discussionmentioning

confidence: 99%

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

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Section: Discussionmentioning

confidence: 99%

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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“…44 On the other hand, two studies, both done with 3.0T strength, reported that ADC values were significantly lower in LVI-positive patients. 45,46 Although various studies (at 1.5T and 3.0T) report significant differences in ADC values when IDC, DCIS and ILC are compared, 36,47,48 the same cannot be said when the relationship between ADC values and parameters such as lymph node involvement and vascular invasion are evaluated. 47,49,50 The Ki-67 index is accepted to accurately define cell proliferation.…”

Section: Discussionmentioning

confidence: 99%

Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma?

Aydın¹,

Güner²,

Bostancı³

et al. 2018

The British Journal of Radiology

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The literature contains studies that report conflicting results which may be caused by differences in B values, ROI area and magnetic field strength. Multicentre studies and systematic reviews of these findings may produce crucial data for the use of DWI in breast cancer. Advances in knowledge: To determine if any significant relationship exists between DWI findings and prognostic factors of breast cancer.

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Erratum to: Peritumoral apparent diffusion coefficients for prediction of lymphovascular invasion in clinically node-negative invasive breast cancer

Cited by 2 publications

References 0 publications

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma?

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